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Breast cancer is the most widely recognized cancer-related death among women globally. Epidemiological studies released in different parts of the world over the past two decades show a significant rise in mortality rates for breast cancer. Today, mammography is the most effective method for imaging masses and microcalcifications in breast tissue. On the other hand, breast biopsy predictions arising from mammogram analysis lead to nearly 70 percent biopsies of benign findings that can be prevented without a biopsy. An automated method is therefore required to assist physicians in mammography analysis prognoses. Researchers have suggested different medical decision support systems recently. In this study, a medical decision support system to be utilized in the process of a breast cancer diagnosis is proposed. The primary purpose of this system is to lower the number of unnecessary breast biopsies and make the diagnosis more reliable. Accordingly, apart from the age of the patient, BI-RADS assessment of the breast tissue, the shape of the mass, mass margin, tissue density, the class label indicating the severity of the lesion are evaluated using the performances of a Naive Bayes algorithm , which is a probabilistic classification algorithm, and Multilayer Perceptron algorithm, which is a feed forward neural network, as two different classification algorithms via a preferred dataset in which each mammography * This paper was presented at the International Conference on Access to Recent Advances in Engineering and Digitalization (ARACONF 2020). ** Sorumlu Yazar: Nevşehir Hacı Bektaş Veli Üniversitesi, Mühendislik Mimarlık Fakültesi, Biyomedikal Mühendisliği Bölümü, Nevşehir, Türkiye, ORCID: 0000-0002-9688-6293, pinarozel@nevsehir.edu.tr European Journal of Science and Technology e-ISSN: 2148-2683 115 mass have six different feature. The proposed system can help to make a biopsy or short-time follow-up decision. The test results are promising that the proposed method can be used as a decision module in computer-aided diagnosis systems.

Sınıflandırma Algoritmalarını Kullanarak Meme Dokusunda Kitleleri Değerlendirmeye Yönelik Karar Destek Sistemi